i-manager Publications

A Review of Predicting Agriculture Yields Based on Machine Learning using Regression and Deep Learning for Diversified Atmosphere and Crops in India

Pradnya P.Rajahans *, Pradnya P. Rajahans**, Ritu Rani***, Radhika Bodhe****

*-*** Trinity College of Engineering and Research, Pune, Maharashtra, India.

Periodicity:January - June'2025

Abstract

Artificial Intelligence (AI) has emerged as a transformative force in agriculture, particularly in crop yield prediction. Accurate forecasting aids farmers and policymakers in making informed decisions regarding resource allocation, risk management, and food security. This paper critically reviews AI-driven crop yield prediction methodologies, focusing on machine learning (ML) and deep learning (DL) approaches. Various techniques including Decision Trees, Random Forest, XG Boost, Artificial Neural Networks (ANNs), and Convolutional Neural Networks (CNNs); are analyzed for their effectiveness in predicting yield outcomes based on meteorological, soil and environmental parameters. Additionally, challenges such as data limitations, model interpretability, and environmental variability are discussed. The review concludes with recommendations for future improvements, including hybrid models, explainable AI, and integration with IoT and remote sensing technologies.

Keywords

Artificial Intelligence, Machine Learning, Deep Learning, Crop Yield Prediction, Precision Agriculture

How to Cite this Article?

Rajahans, P. P., Rani, R., and Bodhe, R. (2025). A Review of Predicting Agriculture Yields Based on Machine Learning using Regression and Deep Learning for Diversified Atmosphere and Crops in India. International Journal of Data Mining Techniques and Applications, 14(1), 34-43.

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